Comparative Performance of Han-Windowed and Unwindowed Adaptive Filter in Processing of Electroencephalographic Signal
Keywords:
Adaptive filter, Electroencephalographic signal, Finite impulse response (FIR), Han-window function, Transfer functionAbstract
An adaptive Finite Impulse Response (FIR) filter was developed and applied to eliminate Electrocardiographic (ECG) artifacts from Electroencephalographic (EEG) signals. The filter utilizes a novel adaptive method, the Han-windowed approach, which adapts the Least Mean Square (LMS) algorithm. The performance of the Han-windowed method was assessed and compared to the standard unwindowed adaptive technique, commonly employed in FIR adaptive filters, demonstrating its effectiveness in removing ECG contamination from EEG data in patients. In scenarios where extremely high accuracy is essential, the windowed adaptive method outperforms the unwindowed approach in terms of signal quality. This is evident from the power spectral densities of both the filtered signals and the contaminated signal when processed with windowed and unwindowed filters at a normalized frequency of 0.1621. When the signal was not passed through a Han-windowed adaptive filter, the simulated and analytical results showed power levels of –14.79dB and –15.79dB, respectively, at the same normalized frequency of 0.1621. These results indicate that, at the same frequency of 0.1621, the Han-windowed adaptive filter provides a 0.42dB improvement over the unwindowed filter, using an optimal filter order of 122 and a step size of 0.0011.
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